Mobile crowdsourcing (MC) has emerged as an efficient data collection and processing technique with the growing use of mobile devices. Mobile devices typically have numerous sensors to capture a variety of data types, including location information, speech, picture, and video data. Due to the lack of storage capacity and processing power of mobile devices, conducting in-depth analysis and computation of the data is impossible. Cloud-based MC is a viable solution to the issue of limited resources in data outsourcing. How to effectively represent and process encrypted heterogeneous data is an enormous challenge. To alleviate this matter, a unified encrypted-tensor model is proposed to represent heterogeneous data consisting of unstructured, semistructured, and structured data, which represents data in different formats and from various sources. Due to the heterogeneity of data, we devise the encrypted query index and implement the query scheme for structured, semistructured, and unstructured data by transforming heterogeneous data into a graph. We evaluated the search performance of our proposed scheme on real-world data sets. This article analyzes the aspects of time search efficiency, memory occupation, and approximation accuracy. Theoretical analysis and experimental results show that the searchable encryption method based on heterogeneous data proposed in this article can effectively represent and mine big data.